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学者姓名：徐硕
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Abstract ：
As technology continues to advance, the integration of generative artificial intelligence tools in various sectors, including education, has gained momentum. ChatGPT, an extensively recognized language model created by OpenAI, has gained significant importance, particularly in education. This study investigates the awareness, acceptance, and adoption of ChatGPT, a state-of-the-art language model developed by OpenAI, in higher education institutions across China. This study applies the partial least squares structural equation modeling (PLS-SEM) method for examining data collected from 320 Chinese university students. The study's conceptual framework integrates key determinants from the Technology Acceptance Model (TAM) and extends it by incorporating perceived intelligence as a critical factor in the adoption process. The study findings reveal that ChatGPT awareness significantly influences the intention to adopt ChatGPT. Perceived ease of use, usefulness, and intelligence significantly mediate the association between ChatGPT awareness and adoption intention of ChatGPT. Additionally, perceived trust significantly moderates the relationship between ChatGPT awareness and perceived ease of use, usefulness, and intelligence. Moving forward, in order to maintain students' critical thinking skills and inventiveness in their assessment writing, assessments must promote the safe use of ChatGPT. Therefore, educators will be crucial in ensuring that artificial intelligence tools are used in assessments ethically and suitably by providing clear guidelines and instructions.
Keyword ：
Perceived usefulness Perceived usefulness Perceived trust Perceived trust ChatGPT awareness ChatGPT awareness Perceived intelligence Perceived intelligence Perceived ease of use Perceived ease of use ChatGPT adoption intention ChatGPT adoption intention
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| GB/T 7714 | Shahzad, Muhammad Farrukh , Xu, Shuo , Javed, Iqra . ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone [J]. | INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION , 2024 , 21 (1) . |
| MLA | Shahzad, Muhammad Farrukh 等. "ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone" . | INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION 21 . 1 (2024) . |
| APA | Shahzad, Muhammad Farrukh , Xu, Shuo , Javed, Iqra . ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone . | INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION , 2024 , 21 (1) . |
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Abstract ：
In the rapidly evolving landscape of industrial and societal progress, technology convergence plays a pivotal role. This dynamic process is usually characterized by the emergence of new nodes and new links. With the long-term and recent interests in predicting technology convergence, link prediction has become the primary approach on the basis of large-scale patent data. Though, the problem of node dynamics is still not addressed in the literature. For this purpose, this paper presents a technology convergence prediction framework with three core modules as follows. (1) A candidate node set is introduced during the network construction phase, mimicking the generation of newly-emerging nodes. (2) An inductive graph representation learning approach is deployed to generate feature vectors for newly-emerging nodes as well as existing ones. (3) The evaluation criteria are revised to shift from the predictable range to the actual predicted range, which can provide a more realistic assessment of predictive performance. Finally, experimental results on the domain of cancer drug development validate the feasibility and effectiveness of our framework in capturing the dynamics of technology convergence, especially concerning the relationships of newly emerged nodes and links. This study provides valuable insights into technology convergence dynamics and points to future research and applications.
Keyword ：
Link prediction Link prediction Technology convergence Technology convergence Inductive graph representation learning Inductive graph representation learning Node dynamics Node dynamics
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| GB/T 7714 | Yang, Guancan , Xing, Jiaxin , Xu, Shuo et al. A framework armed with node dynamics for predicting technology convergence [J]. | JOURNAL OF INFORMETRICS , 2024 , 18 (4) . |
| MLA | Yang, Guancan et al. "A framework armed with node dynamics for predicting technology convergence" . | JOURNAL OF INFORMETRICS 18 . 4 (2024) . |
| APA | Yang, Guancan , Xing, Jiaxin , Xu, Shuo , Zhao, Yuntian . A framework armed with node dynamics for predicting technology convergence . | JOURNAL OF INFORMETRICS , 2024 , 18 (4) . |
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Abstract ：
Artificial Intelligence (AI) technologies have rapidly transformed the education sector and affect student learning performance, particularly in China, a burgeoning educational landscape. The development of generative artificial intelligence (AI) based technologies, such as chatbots and large language models (LLMs) like ChatGPT, has completely changed the educational environment by providing individualized and engaging programs. This study brings forward a model and hypothesis based on social cognitive theory and appropriate research. This investigation centers on how generative AI-based technologies influence students' learning performance in higher education (HE) institutions and the function of self-efficacy, fairness & ethics, creativity, and trust in promoting these connections. Data is collected from 362 students at Chinese universities using purposive sampling. The proposed structural model was evaluated using partial least squares-structural equation modeling (PLS-SEM). The findings reveal that generative AI technologies such as LLM models exemplified by ChatGPT and chatbots significantly influence students' learning performance through self-efficacy, fairness & ethics, and creativity. Furthermore, trust significantly moderates the relationship between fairness & ethics, creativity, and learning performance but negatively moderates the relationship between self-efficacy and learning performance. This study supports the new explanatory potential of social cognitive theory in technological practices. Additionally, this research suggests using generative AI technologies to enhance students' digital learning and boost academic achievement.
Keyword ：
Creativity Creativity Self-efficacy Self-efficacy Generative AI-based technologies Generative AI-based technologies Trust Trust Fairness & ethics Fairness & ethics LLM models LLM models Learning performance Learning performance Higher education Higher education
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| GB/T 7714 | Shahzad, Muhammad Farrukh , Xu, Shuo , Zahid, Hira . Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education [J]. | EDUCATION AND INFORMATION TECHNOLOGIES , 2024 . |
| MLA | Shahzad, Muhammad Farrukh et al. "Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education" . | EDUCATION AND INFORMATION TECHNOLOGIES (2024) . |
| APA | Shahzad, Muhammad Farrukh , Xu, Shuo , Zahid, Hira . Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education . | EDUCATION AND INFORMATION TECHNOLOGIES , 2024 . |
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Abstract ：
Academic inventors bridge science and technology, and have attracted increasing attention. However, little is known about whether they have more diverse research interests than researchers with a single role, and whether their important position for science-technology interactions correlates with their diverse interests. For this purpose, we describe a rule-based approach for matching and identifying academic inventors, and an author interest discovery model with credit allocation schemes is utilized to measure the diversity of each researcher's interests. Finally, extensive empirical results on the DrugBank dataset provide several valuable insights. Contrary to our intuitive expectation, the research interests of academic inventors are the least diverse, while those of authors are the most. In addition, the important position of the researchers has a certain relation with the diversity of research interests. More specifically, the degree of centrality has a significant positive correlation with the diversity of interests, and the constraint presents a significant negative correlation. A significant weaker negative correlation can also be observed between the diversity of research interests of academic inventors and their closeness centrality. The normalized betweenness centrality seems be independent from interest diversity. These conclusions help understand the mechanisms of the important position of academic inventors for science-technology interactions, from the perspective of research interests.
Keyword ：
Science-technology linkage Science-technology linkage Author interest discovery Author interest discovery Interest diversity Interest diversity Academic inventors Academic inventors
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| GB/T 7714 | Xu, Shuo , Li, Ling , An, Xin . Do academic inventors have diverse interests? [J]. | SCIENTOMETRICS , 2023 , 128 (2) : 1023-1053 . |
| MLA | Xu, Shuo et al. "Do academic inventors have diverse interests?" . | SCIENTOMETRICS 128 . 2 (2023) : 1023-1053 . |
| APA | Xu, Shuo , Li, Ling , An, Xin . Do academic inventors have diverse interests? . | SCIENTOMETRICS , 2023 , 128 (2) , 1023-1053 . |
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Abstract ：
Main Path Analysis (MPA) is widely used to trace the developmental trajectory of a technological field through a citation network. The citation-based traversal weight is usually utilized to cherrypick the most significant path. However, the theme of documents along a main path may not be so coherent, and it is very possible to miss the main paths of significant sub-fields overall in a domain. Furthermore, the global path search algorithm in conventional MPA also suffers from high space complexity due to the exhaustive strategy. To address these limitations, a new method, named as semantic MPA (sMPA), is proposed by leveraging semantic information in two steps of candidate path generation and main path selection. In the meanwhile, the resulting source code can be freely accessed. To demonstrate the advantages of our method, extensive experiments are conducted on a patent dataset pertaining to lithium-ion battery in electric vehicle. Experimental results show that our sMPA is capable of discovering more knowledge flows from important subfields, and improving the topical coherence of candidate paths as well.
Keyword ：
Topic coherence Topic coherence Patent mining Patent mining Lithium-ion battery Lithium-ion battery Developmental trajectory Developmental trajectory Main path analysis Main path analysis
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| GB/T 7714 | Chen, Liang , Xu, Shuo , Zhu, Lijun et al. A semantic main path analysis method to identify multiple developmental trajectories [J]. | JOURNAL OF INFORMETRICS , 2022 , 16 (2) . |
| MLA | Chen, Liang et al. "A semantic main path analysis method to identify multiple developmental trajectories" . | JOURNAL OF INFORMETRICS 16 . 2 (2022) . |
| APA | Chen, Liang , Xu, Shuo , Zhu, Lijun , Zhang, Jing , Xu, Haiyun , Yang, Guancan . A semantic main path analysis method to identify multiple developmental trajectories . | JOURNAL OF INFORMETRICS , 2022 , 16 (2) . |
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Abstract ：
Purpose The purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort. Design/methodology/approach An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers. Findings The collaborative patterns can indeed be leveraged to structure properly every annotator's behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench. Originality/value The various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.
Keyword ：
Annotation teamwork Annotation teamwork Knowledge engineering Knowledge engineering Variety-oriented annotation Variety-oriented annotation Annotation workbench Annotation workbench Quality control Quality control
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| GB/T 7714 | Wang, Zheng , Xu, Shuo , Wang, Yibo et al. Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations [J]. | ASLIB JOURNAL OF INFORMATION MANAGEMENT , 2022 , 75 (3) : 523-534 . |
| MLA | Wang, Zheng et al. "Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations" . | ASLIB JOURNAL OF INFORMATION MANAGEMENT 75 . 3 (2022) : 523-534 . |
| APA | Wang, Zheng , Xu, Shuo , Wang, Yibo , Chai, Xiaojiao , Chen, Liang . Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations . | ASLIB JOURNAL OF INFORMATION MANAGEMENT , 2022 , 75 (3) , 523-534 . |
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Abstract ：
Given that citations are not equally important, various techniques have been presented to identify important citations on the basis of supervised machine learning models. However, only a small volume of instances have been annotated manually with the labels. To make full use of unlabeled instances and promote the identification performance, the semi-supervised self-training technique is utilized here to identify important citations in this work. After six groups of features are engineered, the SVM and RF models are chosen as the base classifiers for self-training strategy. Then two experiments based on two different types of datasets are conducted. The experiment on the expert-labeled dataset from one single discipline shows that the semi-supervised versions of SVM and RF models significantly improve the performance of the conventional supervised versions when unannotated samples under 75% and 95% confidence level are rejoined to the training set, respectively. The AUC-PR and AUC-ROC of SVM model are 0.8102 and 0.9622, and those of RF model reach 0.9248 and 0.9841, which outperform their counterparts and the benchmark methods in the literature. This demonstrates the effectiveness of our semi-supervised self-training strategy for important citation identification. Another experiment on the author-labeled dataset from multiple disciplines, semi-supervised learning models can perform better than their supervised learning counterparts in term of AUC-PR when the ratio of labeled instances is less than 20%. Compared to our first experiment, insufficient amount of instances from each discipline in our second experiment enables the performance of the models to be unsatisfactory.
Keyword ：
Semi-supervised learning Semi-supervised learning Self-training Self-training Important citation Important citation Author-labeled dataset Author-labeled dataset Expert-labeled dataset Expert-labeled dataset
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| GB/T 7714 | An, Xin , Sun, Xin , Xu, Shuo . Important citations identification with semi-supervised classification model [J]. | SCIENTOMETRICS , 2022 , 127 (11) : 6533-6555 . |
| MLA | An, Xin et al. "Important citations identification with semi-supervised classification model" . | SCIENTOMETRICS 127 . 11 (2022) : 6533-6555 . |
| APA | An, Xin , Sun, Xin , Xu, Shuo . Important citations identification with semi-supervised classification model . | SCIENTOMETRICS , 2022 , 127 (11) , 6533-6555 . |
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Abstract ：
The deep learning has become an important technique for semantic relation classification in patent texts. Previous studies just borrowed the relevant models from generic texts to patent texts while keeping structure of the models unchanged. Due to significant distinctions between patent texts and generic ones, this enables the performance of these models in the patent texts to be reduced dramatically. To highlight these distinct characteristics in patent texts, seven anno-tated corpora from different fields are comprehensively compared in terms of several indicators for linguistic characteristics. Then, a deep learning based method is proposed to benefit from these characteristics. Our method exploits the information from other similar entity pairs as well as that from the sentences mentioning a focal entity pair. The latter stems from the conventional practices, and the former from our meaningful observation: the stronger the connection between two entity pairs is, the more likely they belong to the same relation type. To measure quantita-tively the connection between two entity pairs, a similarity indicator on the basis of association rules is raised. Extensive experiments on the corpora of TFH-2020 and ChemProt demonstrate that our method for semantic relation classification is capable of benefiting from characteristic of patent texts.
Keyword ：
Linguistic characteristics Linguistic characteristics Deep learning Deep learning Semantic relation classification Semantic relation classification Patent analysis Patent analysis Similarity measure Similarity measure
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| GB/T 7714 | Chen, Liang , Xu, Shuo , Zhu, Lijun et al. A deep learning based method benefiting from characteristics of patents for semantic relation classification [J]. | JOURNAL OF INFORMETRICS , 2022 , 16 (3) . |
| MLA | Chen, Liang et al. "A deep learning based method benefiting from characteristics of patents for semantic relation classification" . | JOURNAL OF INFORMETRICS 16 . 3 (2022) . |
| APA | Chen, Liang , Xu, Shuo , Zhu, Lijun , Zhang, Jing , Yang, Guancan , Xu, Haiyun . A deep learning based method benefiting from characteristics of patents for semantic relation classification . | JOURNAL OF INFORMETRICS , 2022 , 16 (3) . |
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Abstract ：
To build a full picture of previous studies on the origins of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), this paper exploits an active learning-based approach to screen scholarly articles about the origins of SARS-CoV-2 from many scientific publications. In more detail, six seed articles were utilized to manually curate 170 relevant articles and 300 nonrelevant articles. Then, an active learning-based approach with three query strategies and three base classifiers is trained to screen the articles about the origins of SARSCoV- 2. Extensive experimental results show that our active learning-based approach outperforms traditional counterparts, and the uncertain sampling query strategy performs best among the three strategies. By manually checking the top 1,000 articles of each base classifier, we ultimately screened 715 unique scholarly articles to create a publicly available peerreviewed literature corpus, COVID-Origin. This indicates that our approach for screening articles about the origins of SARS-CoV-2 is feasible. © 2022 An et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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| GB/T 7714 | An, X. , Zhang, M. , Xu, S. . An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2 [J]. | PLoS ONE , 2022 , 17 (9 September) . |
| MLA | An, X. et al. "An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2" . | PLoS ONE 17 . 9 September (2022) . |
| APA | An, X. , Zhang, M. , Xu, S. . An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2 . | PLoS ONE , 2022 , 17 (9 September) . |
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Abstract ：
本申请实施例提供了一种主题的探测方法、装置、电子设备及存储介质，涉及信息处理技术领域。该方法包括：获取目标领域中至少两个文本集，并设置预设数量的主题以及主题类别；根据上一次文本集中主题以及主题类别的分配情况，确定本次分配中单词被分配至任意一个主题的第一概率以及被分配至任意一个主题类别的第二概率；根据本次分配中第一概率以及第二概率，对文本集中所有单词分配主题以及主题类别；根据最后一次分配中文本集中每个单词的主题以及主题类别，确定文本集中的主题的分布情况以及主题类别的分布情况。本申请实施例得到了对多源异构文本资源间的科技关联分析更深层次、更可靠的结果。
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| GB/T 7714 | 徐硕 , 李玲 , 翟东升 . 主题的探测方法、装置、电子设备及存储介质 : CN202110049136.1[P]. | 2021-01-14 . |
| MLA | 徐硕 et al. "主题的探测方法、装置、电子设备及存储介质" : CN202110049136.1. | 2021-01-14 . |
| APA | 徐硕 , 李玲 , 翟东升 . 主题的探测方法、装置、电子设备及存储介质 : CN202110049136.1. | 2021-01-14 . |
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